Clear, jargon-free answers to the AI questions people actually ask. Bookmark it, share it, or use it to get your team on the same page.
An AI agent is a software system that uses an AI model to plan and carry out multi-step tasks on its own — choosing actions, using tools and calling other systems to reach a goal. Unlike a chatbot, which answers questions, an agent can take action: book the appointment, update the record, run the workflow. See AI agent vs chatbot →
RAG gives a large language model access to your own documents at answer time. It retrieves the most relevant passages from your knowledge base and feeds them to the model, so answers are grounded in your data — with sources — instead of the model's training alone. It's the main technique for reducing hallucinations. See LLM Optimisation →
Generative AI is AI that creates new content — text, images, code or audio — rather than only classifying or predicting. Large language models such as GPT are the best-known example.
A large language model (LLM) is an AI model trained on vast amounts of text to understand and generate human language. It powers chatbots, assistants and generative-AI features. Examples include GPT, Claude and Llama.
MLOps (machine-learning operations) is the practice of deploying, monitoring and maintaining machine-learning models in production reliably — covering pipelines, versioning, testing and watching for drift. It's essentially DevOps for AI models.
Machine learning is a branch of AI where models learn patterns from data to make predictions or decisions, instead of being explicitly programmed with rules. See our Machine Learning service →
Computer vision is AI that interprets images and video — detecting, classifying and locating objects, reading text or spotting defects — turning visual data into structured information. See our Computer Vision service →
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